A key component to prevention and control of complex disorders, such as alcohol dependence, is to identify genetic and environmental factors that contribute to their development and progression. The analysis is complicated by the fact that genetic and environmental factors (e.g., gender, environmental stressors) interplay in disorder onset and progression. Further, complex disorders are likely to be caused by an interaction between genetic markers; hence the effect of each individual marker (e.g., single-nucleotide polymorphism (SNP)) on risk of a complex disorder is likely to be relatively small. The goals of this project are 1). provide a framework for the analysis of data from case-control studies with the following features: A. risk factors involving a panel of genetic markers that are likely to interact causing the onset of a complex disorder; B. genetic and environmental factors defining subgroups of population with unusual resistance or susceptibility to disorder onset and progression ; 2). apply the proposed methodology to the data collected as a part of Study of Addiction: Genetics and Environment (SAGE) available through the database of Genotypes and Phenotypes. The main difference of proposed approach from the traditional is that PIs consider groups of markers organized based on their function suggested by biomedical experiments, using various levels of genetic information: (1) linkage disequilibrium patterns; (2) genes; (3) pathways. Given the information about what genes work together to influence various aspects of alcohol use, the project aims to investigate the interaction structure of the genetic markers within each group. PIs develop a novel pathway analysis which offers the opportunity to combine association evidence from multiple genetic variants and thus potentially has a better chance of identifying the association between the pathway and the disease. Moreover, they propose a novel methodology for analysis of data in the presence of complex gene-environment interactions, consisting of two major parts: 1. characterize the relevant dependence structure between genetic and environmental factors, (in the case of alcohol dependency, diplotype effects and gender and environmental stressors); 2. Incorporate the dependence structure into the regression analysis. The proposed approach has the potential to improve understanding of genetic predisposition and environmental risk factors of alcoholism through their joint analysis. The approach is flexible and can be adapted for the analysis of other drug dependence disorders. Moreover, the findings of planned data analysis have the potential to challenge the traditional conceptualizations of environmental risk factors and have implications for public health interventions, evaluations and policies.